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基于动态聚类方法的脑电信号片段自动分类及代表性片段提取

Automatic classification of EEG segments and extraction of representative ones by dynamic clusters method.

作者信息

Krajca V

出版信息

Act Nerv Super (Praha). 1984 Jun;26(2):118-28.

PMID:6475475
Abstract

Use of the dynamic clusters method for automatic extraction of compressed information about recorded EEG signal is presented. The computer first divides the record into quasi-stationary segments by means of adaptive segmentation. Second, the extracted segments are classified by a method of dynamic clusters into homogeneous classes. One part of the used clustering algorithm permits to specify and draw the most typical class members, which may represent the whole studied EEG signal and may be used as input for the further phase of the automatic EEG analysis, i.e. for the classification of the whole EEG records. The above procedure was applied to a 75 sec long EEG record of anaesthetized cat intoxicated by CO.

摘要

本文介绍了使用动态聚类方法自动提取记录的脑电图（EEG）信号的压缩信息。计算机首先通过自适应分割将记录划分为准平稳段。其次，利用动态聚类方法将提取的段分类为同类。所使用的聚类算法的一部分允许指定并绘制最典型的类成员，这些成员可以代表整个研究的EEG信号，并可以用作自动EEG分析下一阶段的输入，即用于对整个EEG记录进行分类。上述过程应用于一只被一氧化碳中毒的麻醉猫的75秒长的EEG记录。

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